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...Napster (in 1999), and online music stores (in 2000) were introduced in a relatively short span of time and gained rapid popularity. Consumers of music adapted rapidly to the new environment. In fact, music titles, names of musicians, and music-related technologies (e.g., MP3) have consistently been among the top ten searched items in major Internet search engines since at least the year 2000 (Google, Inc.).
The music industry and its industry association, the Recording Industry Association of America (RIAA), have repeatedly claimed that emerging technologies, especially P2P networks, have negatively impacted their business. RIAA reports that music shipments, both in terms of units shipped and dollar value, have suddenly and sharply declined since 2000 (RIAA 2003). RIAA attributes these dramatic changes directly to the free sharing of music on online P2P systems. This assertion has garnered wide attention and has been the subject of numerous debates (Liebowitz 2004; King 2000a, b; Mathews and Peers 2000; Peers and Gomes 2000; Evangelista 2000). Alexander (2002) viewed P2P technologies as leading to free riders and undermining market efficiencies in the music industry with users obtaining music freely in lieu of legally purchasing the music.
Claiming that the impact of online music sharing on the music business has been devastating, RIAA has aggressively pursued stronger copyright enforcement and regulations (Harmon 2003). RIAA's initial legal strategy was aimed at Napster--RIAA succeeded in shutting down the network largely due to potential liability around Napster's centralized file search technology. The so-called sons of Napster quickly emerged to fill the vacuum, attempting to escape legal wrath by deploying further decentralized structures. In response, RIAA has since altered its legal strategy by seeking sanctions against individuals "who offer a significant number of songs for others to copy" (Zeidler 2003, Bhattacharjee et al. 2006c). But there is an opposing view arguing that P2P systems significantly enhance the ability of users to sample and experience songs. Digital technologies have undoubtedly made information sharing and sampling easier (1) (Bakos et al. 1999, Barua et al. 2001, Brynjolfsson and Smith 2000, Bhattacharjee et al. 2006a) and less costly (Cunningham et al. 2004, Gopal et al. 2004) for individuals. Consumers' increased exposure to music, made possible by P2P systems, also has potential benefits to the music industry. An expert report in the Napster case alludes to the possibility that such online sharing technologies provide sampling mechanisms that may subsequently lead to sales (Fader 2000). The report also argues that the decline in the music industry is due to factors other than P2P-enabled music sharing. Concomitant with the introduction and popularity of P2P systems, the music industry has seen increasing competition for consumer time and resources from nonmusic activities such as video games, DVDs, and online chat rooms (Mathews and Peers 2000, Mathews 2000, Boston 2000) and a downturn in the macroeconomic conditions (e.g., drop in gross domestic product growth rates and employment figures since 2000 through the end of our study period in late 2003).
Empirical evaluation of the impacts of sharing on the success of music products has yielded conflicting results and sparked continued controversy (Liebowitz 2006). Self-reporting bias, sample selection, simultaneity problems, and lack of suitable data to draw the reliable conclusions may all have contributed to contradictory findings. Recent work (Oberholzer and Strumpf 2007) relates downloading activity on two P2P servers with sales of music albums. The authors' data set spans the final 17 weeks of 2002 and was obtained from OpenNap, a relatively small P2P network with a centralized structure as in Napster. Oberholzer and Strumpf (2007, p. 1) found that the effect of downloads on sales is "statistically indistinguishable from zero." However, other studies argue that P2P sharing hurts the music industry (Liebowitz 2006).
The objectives of our study are twofold: (1) assess the impact of recent market and technological developments related to the music industry on survival of music albums on the top 100 charts, and (2) evaluate the specific impact of P2P sharing on album chart survival. We use data on music albums on the top 100 weekly charts together with daily file-sharing activity for these albums on WinMx, one of the most popular file-sharing P2P networks (Pastore 2001; Graham 2005a, b).
Since 1913, Billboard magazine has provided chart information based on sales of music recordings (Gopal et al. 2004). The chart information for the weekly Top 100 albums is based on "a national sample of retail-store sales reports collected, compiled, and provided by Neilsen Soundscan" (Billboard). Appearance and continued presence on the chart has important economic implications and influence on awareness, perceptions, and profits of an album (Bradlow and Fader 2001). Having an album appear on the charts is an important goal of most popular music artists and their record labels (Strobl and Tucker 2000). Our focus is on the survival of albums as measured by the number of weeks an album appears on the top 100 chart before final drop-off. This survival period on the chart captures the "popular life" of an album and has been the object of analysis in a number of studies related to music (Strobl and Tucker 2000, Bradlow and Fader 2001).
Figure 1 illustrates the time frame of analysis for the initial phase of our study. The two-year span, mid-1998 to mid-2000, represents a watershed period in the music industry during which a number of significant events unfolded, including (i) introduction and rapid popularity of MP3 music format, (ii) passage of the Digital Millennium Copyright Act, (iii) introduction and rapid rise in the usage of Napster and P2P networks, (iv) surge in the popularity of DVDs, online chat rooms, and games; and (v) start of a downturn in the overall economy.
The first reported decline in music shipments occurred in 2001, suggesting the possibility that the influence of these events was beginning to be experienced by the music industry. The first phase of our study provides a comparative analysis of album survival before and after the mid-1988 to mid-2000 event window. As depicted in Figure 1, chart information was compiled for three time segments (TSs) before and three after the event window, depicted as pre-TS1 to pre-TS3 and post-TS1 to post-TS3, respectively. In total, over 200 weeks of chart information, spanning the years 1995-2004, was collected for this phase of the study. The following explanatory variables of album survival are analyzed to assess possible changes in impact between the pre- and post-TSs: debut rank of the album, reputation of the artist (as captured by superstar status), the record label that promotes and distributes the album, and artist descriptors (i.e., solo female/solo male/group).
[FIGURE 1 OMITTED]
The second phase of the study attempts to identify the impacts of file sharing on chart success. Our analysis utilizes: (1) data on sharing activity on WinMx for 300+ albums over a period of 60 weeks during 2002 and 2003, (2) corresponding Billboard chart information, and (3) relevant values for other variables detailed above. Our analysis and findings relate only to those albums that appear on the charts. Over 30,000 albums are released each year, but only a small proportion of these appear on the charts. However, this small set of successful albums provides the lion's share of the profits for the record companies (Seabrook 2003).
Our analysis uses sharing that occurs after an album has made an appearance on the charts. We ask the research question: Does the level of sharing influence survival time on the charts? We investigate the impact of sharing in the debut week and also the maximum level of sharing in each of the four-week periods (see details in [section]4). Much of the initial sales of an album are to the so called "committed fan base" (Strobl and Tucker 2000). This core set of consumers are early adopters who have often completed their purchase by the time the album has appeared on the chart. Consequently, the number of weeks an album remains on the chart tends to reflect its receptiveness by the nonhard-core consumers. An impediment in investigating the impact of sharing on album survival is the issue of endogeneity (or omitted variable bias), in that albums that are shared more may also survive longer. Finding an appropriate and strong instrument to address endogeneity is a key requirement in empirical work in this domain, and our paper makes a significant methodological contribution in that regard.
Our expanded analysis offers significant new insights tied to our inclusion of P2P sharing, major/minor label release, and gender of the artist. We find that, overall, sharing has no statistically significant effect on survival. However, a closer analysis reveals that the effect of sharing appears to differ across certain categories. Successful albums (albums that debut high on the chart), are not significantly impacted by sharing. However, online sharing has a low but statistically significant negative effect on survival for less successful (lower debut rank) albums.
Four recording labels (Sony-BMG, Universal, EMI, and Warner Brothers) dominate the music industry and are often referred to as the "major labels." We find that since the occurrence of the significant events outlined above (in the mid-1998 to mid-2000 time frame), the effect of debut rank on chart success has risen whereas the effect of being released by a major label has fallen. In addition, solo female artists perform better than either solo male artists or groups across the periods.
Section 2 discusses related literature that aids in the development of our empirical methodology. Alternative model forms are presented in [section]3. We detail the proportional hazard (PH), accelerated failure time (AFT), and ordinary least squares (OLS) model approaches, illustrating their interrelationships. The details of the data collection are presented in [section]4. Section 5 centers on model estimation. We demonstrate that, for the first phase of our analysis, the estimates of the alternative model forms (PH, AFT, and OLS) are virtually identical. As we address potential omitted variable bias and spurious implication issues using an instrumental variable approach, the second phase of our analysis uses the OLS approach. Section 6 is devoted to a discussion of key findings, their implications, and suggested future research directions....
NOTE: All illustrations and photos
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